Overview

Dataset statistics

Number of variables27
Number of observations609
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory128.6 KiB
Average record size in memory216.2 B

Variable types

Numeric17
Categorical10

Alerts

Barometric Pressure has constant value ""Constant
Heat Index (HI) is highly overall correlated with Heat strokeHigh correlation
Diastolic BP is highly overall correlated with Exertional (1) vs classic (0) and 1 other fieldsHigh correlation
Environmental temperature (C) is highly overall correlated with Heat strokeHigh correlation
Systolic BP is highly overall correlated with Heat strokeHigh correlation
Patient temperature is highly overall correlated with Heat strokeHigh correlation
Rectal temperature (deg C) is highly overall correlated with Exertional (1) vs classic (0) and 1 other fieldsHigh correlation
Relative Humidity is highly overall correlated with Heat strokeHigh correlation
Exposure to sun is highly overall correlated with Heat strokeHigh correlation
Age is highly overall correlated with Heat strokeHigh correlation
Strenuous exercise is highly overall correlated with Heat strokeHigh correlation
Exertional (1) vs classic (0) is highly overall correlated with Diastolic BP and 1 other fieldsHigh correlation
Heat stroke is highly overall correlated with Heat Index (HI) and 9 other fieldsHigh correlation
Cardiovascular disease history is highly imbalanced (72.4%)Imbalance
Dehydration is highly imbalanced (98.2%)Imbalance
Sickle Cell Trait (SCT) is highly imbalanced (96.8%)Imbalance
Exertional (1) vs classic (0) is highly imbalanced (73.1%)Imbalance
Hot/dry skin is highly imbalanced (60.8%)Imbalance
Heat Index (HI) has unique valuesUnique
Weight (kg) has unique valuesUnique
Patient temperature has unique valuesUnique
BMI has unique valuesUnique
Time of day has unique valuesUnique
Exposure to sun has 106 (17.4%) zerosZeros
Strenuous exercise has 106 (17.4%) zerosZeros

Reproduction

Analysis started2023-07-31 07:58:36.048589
Analysis finished2023-07-31 08:00:41.963172
Duration2 minutes and 5.91 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Daily Ingested Water (L)
Real number (ℝ)

Distinct605
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5201188
Minimum1.0055125
Maximum10.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:43.027604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.0055125
5-th percentile1.3041285
Q12.3359406
median3.5010366
Q34.7497758
95-th percentile5.7318461
Maximum10.5
Range9.4944875
Interquartile range (IQR)2.4138353

Descriptive statistics

Standard deviation1.4438382
Coefficient of variation (CV)0.41016746
Kurtosis-0.24877145
Mean3.5201188
Median Absolute Deviation (MAD)1.2057642
Skewness0.2378352
Sum2143.7524
Variance2.0846688
MonotonicityNot monotonic
2023-07-31T10:00:43.888893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 5
 
0.8%
2.451222243 1
 
0.2%
5.605633347 1
 
0.2%
2.571942309 1
 
0.2%
1.252940821 1
 
0.2%
3.671943174 1
 
0.2%
3.653725838 1
 
0.2%
5.345176835 1
 
0.2%
2.349851866 1
 
0.2%
4.938113563 1
 
0.2%
Other values (595) 595
97.7%
ValueCountFrequency (%)
1.005512455 1
0.2%
1.033334278 1
0.2%
1.038642627 1
0.2%
1.065309751 1
0.2%
1.069332819 1
0.2%
1.080717139 1
0.2%
1.084124849 1
0.2%
1.085594401 1
0.2%
1.091423707 1
0.2%
1.102105833 1
0.2%
ValueCountFrequency (%)
10.5 1
0.2%
8.5 1
0.2%
5.990881237 1
0.2%
5.98543918 1
0.2%
5.961174235 1
0.2%
5.958870969 1
0.2%
5.958233578 1
0.2%
5.941470603 1
0.2%
5.929469378 1
0.2%
5.925976251 1
0.2%

Time of year (month)
Real number (ℝ)

Distinct548
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0919749
Minimum0.003923562
Maximum11.97782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:44.964198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.003923562
5-th percentile0.53703149
Q13.0037778
median6.4408388
Q39.0518566
95-th percentile11.276925
Maximum11.97782
Range11.973896
Interquartile range (IQR)6.0480788

Descriptive statistics

Standard deviation3.4710435
Coefficient of variation (CV)0.56977312
Kurtosis-1.1909987
Mean6.0919749
Median Absolute Deviation (MAD)3.1068162
Skewness-0.04868991
Sum3710.0127
Variance12.048143
MonotonicityNot monotonic
2023-07-31T10:00:45.745144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 44
 
7.2%
11 18
 
3.0%
8 2
 
0.3%
11.20522196 1
 
0.2%
11.14391055 1
 
0.2%
3.246291794 1
 
0.2%
8.226818455 1
 
0.2%
6.284612336 1
 
0.2%
5.056796021 1
 
0.2%
0.313058443 1
 
0.2%
Other values (538) 538
88.3%
ValueCountFrequency (%)
0.003923562 1
0.2%
0.009008768 1
0.2%
0.035254987 1
0.2%
0.03881845 1
0.2%
0.048821161 1
0.2%
0.049501563 1
0.2%
0.053254833 1
0.2%
0.077457022 1
0.2%
0.08642582 1
0.2%
0.10722331 1
0.2%
ValueCountFrequency (%)
11.9778195 1
0.2%
11.96482549 1
0.2%
11.95771606 1
0.2%
11.91704793 1
0.2%
11.91223025 1
0.2%
11.87535639 1
0.2%
11.83339585 1
0.2%
11.80826101 1
0.2%
11.78149471 1
0.2%
11.75930648 1
0.2%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
580 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580
95.2%
1 29
 
4.8%

Length

2023-07-31T10:00:46.217593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:00:46.747114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 580
95.2%
1 29
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 580
95.2%
1 29
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 580
95.2%
1 29
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 580
95.2%
1 29
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 580
95.2%
1 29
 
4.8%

Dehydration
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
608 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 608
99.8%
1 1
 
0.2%

Length

2023-07-31T10:00:47.282432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:00:47.968731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 608
99.8%
1 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 608
99.8%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 608
99.8%
1 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 608
99.8%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 608
99.8%
1 1
 
0.2%

Heat Index (HI)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct609
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.497405
Minimum31.859605
Maximum122.7492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:48.621795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum31.859605
5-th percentile57.584509
Q173.00526
median83.461479
Q398.687987
95-th percentile108.83411
Maximum122.7492
Range90.8896
Interquartile range (IQR)25.682727

Descriptive statistics

Standard deviation16.266051
Coefficient of variation (CV)0.19250356
Kurtosis-0.58228461
Mean84.497405
Median Absolute Deviation (MAD)12.690432
Skewness-0.18355742
Sum51458.92
Variance264.58441
MonotonicityNot monotonic
2023-07-31T10:00:49.616715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107.2969103 1
 
0.2%
69.04324363 1
 
0.2%
70.37325039 1
 
0.2%
75.75731614 1
 
0.2%
91.99150959 1
 
0.2%
91.94963123 1
 
0.2%
89.7811142 1
 
0.2%
60.33056852 1
 
0.2%
83.56554077 1
 
0.2%
80.78552386 1
 
0.2%
Other values (599) 599
98.4%
ValueCountFrequency (%)
31.85960502 1
0.2%
39.6851692 1
0.2%
40.89342084 1
0.2%
44.93927216 1
0.2%
45.10685487 1
0.2%
45.45062658 1
0.2%
45.64947685 1
0.2%
47.57054621 1
0.2%
47.80030442 1
0.2%
48.59836896 1
0.2%
ValueCountFrequency (%)
122.7492049 1
0.2%
116.7575655 1
0.2%
115.6930483 1
0.2%
115.663311 1
0.2%
114.352476 1
0.2%
113.8151109 1
0.2%
113.3894519 1
0.2%
113.1365327 1
0.2%
112.7169043 1
0.2%
112.4496248 1
0.2%

Diastolic BP
Real number (ℝ)

Distinct542
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.235813
Minimum0
Maximum116
Zeros6
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:50.433069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q181.388233
median84.166226
Q387.080987
95-th percentile89.490055
Maximum116
Range116
Interquartile range (IQR)5.6927538

Descriptive statistics

Standard deviation13.365608
Coefficient of variation (CV)0.16452852
Kurtosis15.189897
Mean81.235813
Median Absolute Deviation (MAD)2.8837048
Skewness-3.476408
Sum49472.61
Variance178.63949
MonotonicityNot monotonic
2023-07-31T10:00:51.072222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 17
 
2.8%
50 12
 
2.0%
80 10
 
1.6%
70 8
 
1.3%
40 8
 
1.3%
0 6
 
1.0%
100 5
 
0.8%
30 4
 
0.7%
110 2
 
0.3%
74 2
 
0.3%
Other values (532) 535
87.8%
ValueCountFrequency (%)
0 6
 
1.0%
20 1
 
0.2%
30 4
 
0.7%
40 8
1.3%
44 1
 
0.2%
45 1
 
0.2%
50 12
2.0%
55 2
 
0.3%
60 17
2.8%
64 1
 
0.2%
ValueCountFrequency (%)
116 1
 
0.2%
110 2
 
0.3%
100 5
0.8%
92 1
 
0.2%
90 2
 
0.3%
89.95624137 1
 
0.2%
89.93899301 1
 
0.2%
89.93164408 1
 
0.2%
89.87494698 1
 
0.2%
89.87046882 1
 
0.2%
Distinct568
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.277937
Minimum14.133432
Maximum44.271485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:51.855158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.133432
5-th percentile21.64853
Q127.459657
median31.157351
Q335.509664
95-th percentile40.214313
Maximum44.271485
Range30.138052
Interquartile range (IQR)8.0500074

Descriptive statistics

Standard deviation5.7065093
Coefficient of variation (CV)0.1824452
Kurtosis-0.43587897
Mean31.277937
Median Absolute Deviation (MAD)4.0179801
Skewness-0.13694201
Sum19048.264
Variance32.564249
MonotonicityNot monotonic
2023-07-31T10:00:52.670464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 14
 
2.3%
41.11111111 9
 
1.5%
38.88888889 7
 
1.1%
37.7 7
 
1.1%
40.55555556 5
 
0.8%
39.44444444 4
 
0.7%
32.77777778 2
 
0.3%
38.21571498 1
 
0.2%
24.8988322 1
 
0.2%
33.30066774 1
 
0.2%
Other values (558) 558
91.6%
ValueCountFrequency (%)
14.1334325 1
0.2%
14.42939209 1
0.2%
16.79140627 1
0.2%
17.23762456 1
0.2%
17.30725898 1
0.2%
17.34572342 1
0.2%
18.17058176 1
0.2%
18.77651178 1
0.2%
19.04177453 1
0.2%
19.08050002 1
0.2%
ValueCountFrequency (%)
44.27148451 1
0.2%
44.22754371 1
0.2%
43.78700583 1
0.2%
43.2018912 1
0.2%
42.90040543 1
0.2%
42.69557021 1
0.2%
42.46550876 1
0.2%
42.35277186 1
0.2%
42.15036495 1
0.2%
41.8480826 1
0.2%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
607 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 607
99.7%
1 2
 
0.3%

Length

2023-07-31T10:00:53.389100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:00:54.015666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 607
99.7%
1 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 607
99.7%
1 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 607
99.7%
1 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 607
99.7%
1 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 607
99.7%
1 2
 
0.3%

Systolic BP
Real number (ℝ)

Distinct549
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.32361
Minimum0
Maximum219
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:54.447555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1111.89942
median114.59001
Q3117.39328
95-th percentile120
Maximum219
Range219
Interquartile range (IQR)5.4938552

Descriptive statistics

Standard deviation15.319954
Coefficient of variation (CV)0.13400515
Kurtosis20.463025
Mean114.32361
Median Absolute Deviation (MAD)2.7530639
Skewness-0.30626325
Sum69623.081
Variance234.70098
MonotonicityNot monotonic
2023-07-31T10:00:55.091033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 15
 
2.5%
110 8
 
1.3%
80 7
 
1.1%
90 7
 
1.1%
130 6
 
1.0%
70 5
 
0.8%
170 4
 
0.7%
120 4
 
0.7%
140 3
 
0.5%
160 3
 
0.5%
Other values (539) 547
89.8%
ValueCountFrequency (%)
0 2
 
0.3%
50 2
 
0.3%
55 1
 
0.2%
60 2
 
0.3%
68 1
 
0.2%
70 5
0.8%
80 7
1.1%
84 1
 
0.2%
90 7
1.1%
95 1
 
0.2%
ValueCountFrequency (%)
219 1
 
0.2%
210 1
 
0.2%
190 1
 
0.2%
175 1
 
0.2%
174 1
 
0.2%
170 4
0.7%
165 2
0.3%
160 3
0.5%
150 3
0.5%
146 1
 
0.2%

Weight (kg)
Real number (ℝ)

Distinct609
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.751283
Minimum41.27903
Maximum151.953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:55.940774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum41.27903
5-th percentile41.859372
Q144.442865
median47.64735
Q350.594373
95-th percentile53.114621
Maximum151.953
Range110.67397
Interquartile range (IQR)6.1515073

Descriptive statistics

Standard deviation5.5483511
Coefficient of variation (CV)0.11619271
Kurtosis203.95893
Mean47.751283
Median Absolute Deviation (MAD)3.0627906
Skewness10.871289
Sum29080.531
Variance30.7842
MonotonicityNot monotonic
2023-07-31T10:00:56.639879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.36407871 1
 
0.2%
44.5025297 1
 
0.2%
45.13385706 1
 
0.2%
42.24747752 1
 
0.2%
51.07149857 1
 
0.2%
51.07755199 1
 
0.2%
52.98638271 1
 
0.2%
43.56969563 1
 
0.2%
47.6176145 1
 
0.2%
42.77154242 1
 
0.2%
Other values (599) 599
98.4%
ValueCountFrequency (%)
41.2790305 1
0.2%
41.28314751 1
0.2%
41.31130354 1
0.2%
41.34998882 1
0.2%
41.38752577 1
0.2%
41.41152545 1
0.2%
41.42571152 1
0.2%
41.4332921 1
0.2%
41.45077274 1
0.2%
41.4609715 1
0.2%
ValueCountFrequency (%)
151.953 1
0.2%
53.51857115 1
0.2%
53.50834001 1
0.2%
53.49671171 1
0.2%
53.45663092 1
0.2%
53.4488318 1
0.2%
53.42024573 1
0.2%
53.41823932 1
0.2%
53.38738438 1
0.2%
53.38702136 1
0.2%

Patient temperature
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct609
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.798344
Minimum32.793188
Maximum44.165529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:57.448891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum32.793188
5-th percentile34.407288
Q135.597859
median36.408496
Q337.441841
95-th percentile40.993188
Maximum44.165529
Range11.37234
Interquartile range (IQR)1.8439817

Descriptive statistics

Standard deviation1.9489093
Coefficient of variation (CV)0.052961877
Kurtosis1.6331586
Mean36.798344
Median Absolute Deviation (MAD)0.93292322
Skewness1.215229
Sum22410.191
Variance3.7982476
MonotonicityNot monotonic
2023-07-31T10:00:58.041366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.8 1
 
0.2%
35.93170144 1
 
0.2%
36.76608319 1
 
0.2%
35.7339726 1
 
0.2%
37.16014007 1
 
0.2%
37.57082476 1
 
0.2%
36.88972679 1
 
0.2%
36.11298677 1
 
0.2%
38.09443226 1
 
0.2%
36.5426797 1
 
0.2%
Other values (599) 599
98.4%
ValueCountFrequency (%)
32.79318825 1
0.2%
33.11327974 1
0.2%
33.12947351 1
0.2%
33.14310925 1
0.2%
33.27787399 1
0.2%
33.43464544 1
0.2%
33.48524789 1
0.2%
33.62285462 1
0.2%
33.68673587 1
0.2%
33.75125842 1
0.2%
ValueCountFrequency (%)
44.16552856 1
0.2%
44.15315249 1
0.2%
43.49778324 1
0.2%
43.43868735 1
0.2%
43.20612898 1
0.2%
43.18663776 1
0.2%
43.03426806 1
0.2%
43 1
0.2%
42.7916102 1
0.2%
42.73021967 1
0.2%
Distinct556
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.59703
Minimum36.100453
Maximum44.444444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:00:58.794852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum36.100453
5-th percentile36.16168
Q136.430223
median36.783068
Q337.093357
95-th percentile42.666667
Maximum44.444444
Range8.3439914
Interquartile range (IQR)0.66313343

Descriptive statistics

Standard deviation2.1317362
Coefficient of variation (CV)0.056699591
Kurtosis1.863931
Mean37.59703
Median Absolute Deviation (MAD)0.33121158
Skewness1.8481931
Sum22896.591
Variance4.5442994
MonotonicityNot monotonic
2023-07-31T10:00:59.182347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 7
 
1.1%
42.6 6
 
1.0%
42.5 5
 
0.8%
41.9 4
 
0.7%
44.44444444 4
 
0.7%
42.66666667 4
 
0.7%
40 4
 
0.7%
42.22222222 4
 
0.7%
43 3
 
0.5%
43.1 3
 
0.5%
Other values (546) 565
92.8%
ValueCountFrequency (%)
36.10045303 1
0.2%
36.10079926 1
0.2%
36.10263086 1
0.2%
36.10302206 1
0.2%
36.10639033 1
0.2%
36.10718555 1
0.2%
36.11239777 1
0.2%
36.11432526 1
0.2%
36.11886135 1
0.2%
36.12007882 1
0.2%
ValueCountFrequency (%)
44.44444444 4
0.7%
44.35050299 1
 
0.2%
43.9 1
 
0.2%
43.88888889 3
0.5%
43.68749815 1
 
0.2%
43.55555556 1
 
0.2%
43.33333333 2
0.3%
43.22222222 1
 
0.2%
43.11111111 1
 
0.2%
43.1 3
0.5%

Relative Humidity
Real number (ℝ)

Distinct510
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12907252
Minimum-0.17878492
Maximum0.70666667
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)12.6%
Memory size4.9 KiB
2023-07-31T10:00:59.586354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.17878492
5-th percentile-0.054426381
Q10.047920777
median0.1
Q30.18464277
95-th percentile0.42333333
Maximum0.70666667
Range0.88545159
Interquartile range (IQR)0.13672199

Descriptive statistics

Standard deviation0.13030406
Coefficient of variation (CV)1.0095414
Kurtosis1.9276713
Mean0.12907252
Median Absolute Deviation (MAD)0.064358597
Skewness1.0892377
Sum78.605165
Variance0.016979147
MonotonicityNot monotonic
2023-07-31T10:01:00.002941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 65
 
10.7%
0.493333333 14
 
2.3%
0.423333333 9
 
1.5%
0.473333333 6
 
1.0%
0.416666667 6
 
1.0%
0.43 4
 
0.7%
0.706666667 2
 
0.3%
0.4 1
 
0.2%
0.174931063 1
 
0.2%
0.319808446 1
 
0.2%
Other values (500) 500
82.1%
ValueCountFrequency (%)
-0.178784919 1
0.2%
-0.140718063 1
0.2%
-0.134074602 1
0.2%
-0.124927961 1
0.2%
-0.12026436 1
0.2%
-0.115251137 1
0.2%
-0.109801677 1
0.2%
-0.102440013 1
0.2%
-0.100163122 1
0.2%
-0.095364078 1
0.2%
ValueCountFrequency (%)
0.706666667 2
 
0.3%
0.493333333 14
2.3%
0.473333333 6
1.0%
0.43 4
 
0.7%
0.424210205 1
 
0.2%
0.423333333 9
1.5%
0.416666667 6
1.0%
0.409681569 1
 
0.2%
0.4 1
 
0.2%
0.36480695 1
 
0.2%

Exposure to sun
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct503
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24799035
Minimum0
Maximum1
Zeros106
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:01:00.469085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.15860663
median0.26969024
Q30.35004687
95-th percentile0.46095787
Maximum1
Range1
Interquartile range (IQR)0.19144025

Descriptive statistics

Standard deviation0.15237344
Coefficient of variation (CV)0.61443292
Kurtosis0.91497014
Mean0.24799035
Median Absolute Deviation (MAD)0.093993994
Skewness0.0190533
Sum151.02612
Variance0.023217664
MonotonicityNot monotonic
2023-07-31T10:01:00.902244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 106
 
17.4%
1 2
 
0.3%
0.323543722 1
 
0.2%
0.225079111 1
 
0.2%
0.246563747 1
 
0.2%
0.39063665 1
 
0.2%
0.313983377 1
 
0.2%
0.319307157 1
 
0.2%
0.38707547 1
 
0.2%
0.354407687 1
 
0.2%
Other values (493) 493
81.0%
ValueCountFrequency (%)
0 106
17.4%
0.011489015 1
 
0.2%
0.022716548 1
 
0.2%
0.033200094 1
 
0.2%
0.034468082 1
 
0.2%
0.057117075 1
 
0.2%
0.064629429 1
 
0.2%
0.068345441 1
 
0.2%
0.071623973 1
 
0.2%
0.07530167 1
 
0.2%
ValueCountFrequency (%)
1 2
0.3%
0.607292586 1
0.2%
0.597998996 1
0.2%
0.588578478 1
0.2%
0.547723051 1
0.2%
0.534119342 1
0.2%
0.534105973 1
0.2%
0.508233792 1
0.2%
0.500248116 1
0.2%
0.5 1
0.2%

BMI
Real number (ℝ)

Distinct609
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.849753
Minimum18.505636
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:01:01.272570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18.505636
5-th percentile18.773903
Q119.816563
median20.887468
Q321.914046
95-th percentile22.779454
Maximum24
Range5.4943639
Interquartile range (IQR)2.0974832

Descriptive statistics

Standard deviation1.2692681
Coefficient of variation (CV)0.060876888
Kurtosis-1.1063012
Mean20.849753
Median Absolute Deviation (MAD)1.0436291
Skewness-0.074701763
Sum12697.5
Variance1.6110414
MonotonicityNot monotonic
2023-07-31T10:01:01.652662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 1
 
0.2%
22.11445384 1
 
0.2%
21.46139443 1
 
0.2%
22.0554344 1
 
0.2%
22.02466286 1
 
0.2%
21.27420418 1
 
0.2%
20.28688098 1
 
0.2%
19.52146305 1
 
0.2%
22.43788408 1
 
0.2%
19.15818117 1
 
0.2%
Other values (599) 599
98.4%
ValueCountFrequency (%)
18.50563606 1
0.2%
18.51065791 1
0.2%
18.52649768 1
0.2%
18.53175632 1
0.2%
18.55140791 1
0.2%
18.57600936 1
0.2%
18.57657739 1
0.2%
18.57993796 1
0.2%
18.5852663 1
0.2%
18.58547754 1
0.2%
ValueCountFrequency (%)
24 1
0.2%
22.99490118 1
0.2%
22.99028723 1
0.2%
22.97231228 1
0.2%
22.96441321 1
0.2%
22.96093558 1
0.2%
22.95993069 1
0.2%
22.95857319 1
0.2%
22.9528963 1
0.2%
22.95007151 1
0.2%

Exertional (1) vs classic (0)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
581 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 581
95.4%
1 28
 
4.6%

Length

2023-07-31T10:01:02.037411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:01:02.375414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 581
95.4%
1 28
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 581
95.4%
1 28
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 581
95.4%
1 28
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 581
95.4%
1 28
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 581
95.4%
1 28
 
4.6%
Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
29.97
609 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3045
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.97
2nd row29.97
3rd row29.97
4th row29.97
5th row29.97

Common Values

ValueCountFrequency (%)
29.97 609
100.0%

Length

2023-07-31T10:01:02.677873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:01:03.006977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
29.97 609
100.0%

Most occurring characters

ValueCountFrequency (%)
9 1218
40.0%
2 609
20.0%
. 609
20.0%
7 609
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2436
80.0%
Other Punctuation 609
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1218
50.0%
2 609
25.0%
7 609
25.0%
Other Punctuation
ValueCountFrequency (%)
. 609
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3045
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 1218
40.0%
2 609
20.0%
. 609
20.0%
7 609
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3045
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1218
40.0%
2 609
20.0%
. 609
20.0%
7 609
20.0%
Distinct563
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.42079
Minimum41.647529
Maximum210.29013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:01:03.328790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum41.647529
5-th percentile73.635971
Q1102.8516
median121.43981
Q3136.5874
95-th percentile167.50989
Maximum210.29013
Range168.6426
Interquartile range (IQR)33.735807

Descriptive statistics

Standard deviation27.761283
Coefficient of variation (CV)0.23053564
Kurtosis0.20178132
Mean120.42079
Median Absolute Deviation (MAD)17.339559
Skewness0.041099581
Sum73336.259
Variance770.68884
MonotonicityNot monotonic
2023-07-31T10:01:03.736847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 11
 
1.8%
140 8
 
1.3%
130 6
 
1.0%
110 6
 
1.0%
128 5
 
0.8%
96 4
 
0.7%
104 3
 
0.5%
168 3
 
0.5%
90 2
 
0.3%
160 2
 
0.3%
Other values (553) 559
91.8%
ValueCountFrequency (%)
41.64752888 1
0.2%
43.94986445 1
0.2%
44.47877979 1
0.2%
47.64004614 1
0.2%
51.59046989 1
0.2%
51.61254333 1
0.2%
53.0737656 1
0.2%
54.68643925 1
0.2%
58.65408989 1
0.2%
58.90579649 1
0.2%
ValueCountFrequency (%)
210.2901321 1
0.2%
201.0707689 1
0.2%
198.8023879 1
0.2%
193.2096951 1
0.2%
188.1562656 1
0.2%
186.7409331 1
0.2%
186.5737672 1
0.2%
186.3493928 1
0.2%
186 1
0.2%
185.3775516 1
0.2%

Age
Real number (ℝ)

Distinct540
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.129276
Minimum12
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:01:04.249780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile19.717292
Q128.676784
median40.462594
Q351.379236
95-th percentile65
Maximum90
Range78
Interquartile range (IQR)22.702452

Descriptive statistics

Standard deviation14.501809
Coefficient of variation (CV)0.35259091
Kurtosis-0.32278159
Mean41.129276
Median Absolute Deviation (MAD)11.428795
Skewness0.38051154
Sum25047.729
Variance210.30246
MonotonicityNot monotonic
2023-07-31T10:01:04.825326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 15
 
2.5%
50 11
 
1.8%
60 8
 
1.3%
65 7
 
1.1%
70 7
 
1.1%
78.4 7
 
1.1%
63 5
 
0.8%
64 4
 
0.7%
67 3
 
0.5%
25 3
 
0.5%
Other values (530) 539
88.5%
ValueCountFrequency (%)
12 1
0.2%
18.01247225 1
0.2%
18.04939716 1
0.2%
18.06575232 1
0.2%
18.13667624 1
0.2%
18.15266357 1
0.2%
18.2626375 1
0.2%
18.46370739 1
0.2%
18.53912849 1
0.2%
18.57970111 1
0.2%
ValueCountFrequency (%)
90 1
 
0.2%
84 1
 
0.2%
81 1
 
0.2%
80 1
 
0.2%
78.4 7
1.1%
76 1
 
0.2%
75 1
 
0.2%
74 1
 
0.2%
72 2
 
0.3%
71 1
 
0.2%

Sweating
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
340 
1
269 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 340
55.8%
1 269
44.2%

Length

2023-07-31T10:01:05.344264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:01:05.722183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 340
55.8%
1 269
44.2%

Most occurring characters

ValueCountFrequency (%)
0 340
55.8%
1 269
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 340
55.8%
1 269
44.2%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 340
55.8%
1 269
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 340
55.8%
1 269
44.2%
Distinct582
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082873967
Minimum0
Maximum1
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:01:06.100245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00508138
Q10.023166085
median0.049303372
Q30.073177618
95-th percentile0.08870706
Maximum1
Range1
Interquartile range (IQR)0.050011533

Descriptive statistics

Standard deviation0.18237792
Coefficient of variation (CV)2.200666
Kurtosis20.497512
Mean0.082873967
Median Absolute Deviation (MAD)0.024800032
Skewness4.6554718
Sum50.470246
Variance0.033261705
MonotonicityNot monotonic
2023-07-31T10:01:06.600480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22
 
3.6%
0 5
 
0.8%
0.5 3
 
0.5%
0.03603683 1
 
0.2%
0.084888763 1
 
0.2%
0.046649401 1
 
0.2%
0.024254606 1
 
0.2%
0.060861075 1
 
0.2%
0.028556985 1
 
0.2%
0.049934028 1
 
0.2%
Other values (572) 572
93.9%
ValueCountFrequency (%)
0 5
0.8%
0.000145159 1
 
0.2%
0.000178862 1
 
0.2%
0.000495914 1
 
0.2%
0.000538519 1
 
0.2%
0.000637733 1
 
0.2%
0.000766546 1
 
0.2%
0.001149967 1
 
0.2%
0.001332115 1
 
0.2%
0.001781453 1
 
0.2%
ValueCountFrequency (%)
1 22
3.6%
0.5 3
 
0.5%
0.089953226 1
 
0.2%
0.089880277 1
 
0.2%
0.089224612 1
 
0.2%
0.089096435 1
 
0.2%
0.089019879 1
 
0.2%
0.088968707 1
 
0.2%
0.08831459 1
 
0.2%
0.087974765 1
 
0.2%

Strenuous exercise
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct503
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38811684
Minimum0
Maximum1
Zeros106
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:01:07.324155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.11113292
median0.37710862
Q30.63918389
95-th percentile0.85476639
Maximum1
Range1
Interquartile range (IQR)0.52805097

Descriptive statistics

Standard deviation0.29298287
Coefficient of variation (CV)0.75488316
Kurtosis-1.3108457
Mean0.38811684
Median Absolute Deviation (MAD)0.2650672
Skewness0.10367183
Sum236.36316
Variance0.085838961
MonotonicityNot monotonic
2023-07-31T10:01:07.794318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 106
 
17.4%
1 2
 
0.3%
0.414407245 1
 
0.2%
0.597086478 1
 
0.2%
0.766789251 1
 
0.2%
0.217756793 1
 
0.2%
0.644311803 1
 
0.2%
0.315923179 1
 
0.2%
0.801194117 1
 
0.2%
0.580370419 1
 
0.2%
Other values (493) 493
81.0%
ValueCountFrequency (%)
0 106
17.4%
0.002649533 1
 
0.2%
0.006923737 1
 
0.2%
0.008059051 1
 
0.2%
0.008752593 1
 
0.2%
0.009693951 1
 
0.2%
0.011087891 1
 
0.2%
0.011466743 1
 
0.2%
0.012083274 1
 
0.2%
0.014510469 1
 
0.2%
ValueCountFrequency (%)
1 2
0.3%
0.89801375 1
0.2%
0.89526719 1
0.2%
0.893489879 1
0.2%
0.892073917 1
0.2%
0.891915455 1
0.2%
0.888242952 1
0.2%
0.886615003 1
0.2%
0.885473362 1
0.2%
0.884604789 1
0.2%

Nationality
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
343 
0
266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 343
56.3%
0 266
43.7%

Length

2023-07-31T10:01:08.158099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:01:08.421690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 343
56.3%
0 266
43.7%

Most occurring characters

ValueCountFrequency (%)
1 343
56.3%
0 266
43.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 343
56.3%
0 266
43.7%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 343
56.3%
0 266
43.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 343
56.3%
0 266
43.7%

Sex
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
314 
0
295 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 314
51.6%
0 295
48.4%

Length

2023-07-31T10:01:08.686021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:01:09.058056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 314
51.6%
0 295
48.4%

Most occurring characters

ValueCountFrequency (%)
1 314
51.6%
0 295
48.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 314
51.6%
0 295
48.4%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 314
51.6%
0 295
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 314
51.6%
0 295
48.4%

Hot/dry skin
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
562 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 562
92.3%
1 47
 
7.7%

Length

2023-07-31T10:01:09.357805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:01:09.675337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 562
92.3%
1 47
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 562
92.3%
1 47
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 562
92.3%
1 47
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 562
92.3%
1 47
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 562
92.3%
1 47
 
7.7%

Time of day
Real number (ℝ)

Distinct609
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.819241
Minimum9.002272
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-31T10:01:09.975785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.002272
5-th percentile9.3390111
Q110.928394
median12.77323
Q314.657338
95-th percentile16.483148
Maximum17
Range7.997728
Interquartile range (IQR)3.7289438

Descriptive statistics

Standard deviation2.2705317
Coefficient of variation (CV)0.17711904
Kurtosis-1.1038859
Mean12.819241
Median Absolute Deviation (MAD)1.8649343
Skewness0.093769659
Sum7806.918
Variance5.1553143
MonotonicityNot monotonic
2023-07-31T10:01:10.415272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.55062835 1
 
0.2%
12.97365408 1
 
0.2%
12.73994646 1
 
0.2%
12.42814499 1
 
0.2%
10.19043989 1
 
0.2%
13.02203931 1
 
0.2%
13.02830869 1
 
0.2%
11.39290695 1
 
0.2%
15.41699427 1
 
0.2%
16.36575115 1
 
0.2%
Other values (599) 599
98.4%
ValueCountFrequency (%)
9.002272031 1
0.2%
9.026198637 1
0.2%
9.031242258 1
0.2%
9.043544081 1
0.2%
9.045944762 1
0.2%
9.049473547 1
0.2%
9.056239921 1
0.2%
9.060058794 1
0.2%
9.078117669 1
0.2%
9.109083709 1
0.2%
ValueCountFrequency (%)
17 1
0.2%
16.99442197 1
0.2%
16.98171542 1
0.2%
16.96843146 1
0.2%
16.96493708 1
0.2%
16.96340711 1
0.2%
16.95601056 1
0.2%
16.94904082 1
0.2%
16.92739646 1
0.2%
16.92712598 1
0.2%

Heat stroke
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
500 
1
109 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters609
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 500
82.1%
1 109
 
17.9%

Length

2023-07-31T10:01:10.830442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T10:01:11.168731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 500
82.1%
1 109
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 500
82.1%
1 109
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 500
82.1%
1 109
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 500
82.1%
1 109
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 500
82.1%
1 109
 
17.9%

Interactions

2023-07-31T10:00:31.802506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:50.793422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:59.239158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:05.083378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:08.939358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:13.368144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:17.235172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:21.663233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:29.092947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:37.649350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:43.582972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:49.715285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:56.577792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:03.055102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:10.401518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:16.972703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:24.030787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:32.223884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:52.681317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:59.517188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:05.328039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:09.163321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:13.570278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:17.461580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:22.116361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:29.697683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:37.921573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:43.954700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:50.118342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:56.972721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:03.380520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:10.758142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:17.616528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:24.514006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:32.680338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:53.659277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:59.830085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:05.535477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:09.406936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:13.750078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:17.952944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:22.565392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:30.283482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:38.240972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:44.290144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:50.485002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:57.375367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:03.799683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:11.151274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:18.010832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:25.075401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:33.067339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:54.189545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:00.092615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:05.746607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:09.623322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:13.916556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:18.122915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:22.909769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:30.715564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:38.538462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:44.609153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:50.820262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:57.744762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:04.227365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:11.542037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:18.367342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:25.481367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:33.460260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:54.629728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:00.330850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:05.960116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:09.878489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:14.098331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:18.314992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:23.443304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:31.108402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:38.805563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:44.994999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:51.181060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:58.118615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:04.634892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:11.941130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:18.760424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:25.858898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:33.835351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:55.074230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:00.577951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:06.160318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:10.127963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:14.282217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:18.596313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:23.737552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:31.670539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:39.300273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:45.390105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:51.564222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:58.521774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:05.082376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:12.349917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:19.176466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T10:00:34.166294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T09:59:00.820412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:06.371391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:10.807571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:14.446915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:18.820747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:24.038849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:32.133088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:39.692146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:45.762510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:51.912347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:58.866312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:05.832128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:12.704230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:19.553824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:26.752215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:34.560062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:56.000042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:01.086904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:06.564625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:11.039631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:14.674990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:19.070569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:24.335302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:32.829654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:40.037728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:46.138297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:52.298245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:59.219528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:06.282664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:13.091045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:19.932162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T09:59:06.761582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:11.295703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:14.895986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:19.302563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:24.788682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:33.614088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:40.424031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:46.487200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T10:00:27.602882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:35.405974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:56.790781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:01.573472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:06.948353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:11.496300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:15.152748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:19.547442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:25.135285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:34.469586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T09:58:57.032584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:02.978992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:07.195107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T09:59:15.414509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:19.799119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:25.409276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:35.181286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:41.078972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:47.191221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:53.617010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:00.382024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:07.495744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:14.182969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T09:59:15.927798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:20.344315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:25.991538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:35.969463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:41.622822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:47.939157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:54.349395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:01.187067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:08.282359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:14.961653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:21.862215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:29.160664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T09:58:57.932560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:03.745388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:07.950640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:12.427843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:16.194188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:20.600342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:26.328435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:36.288171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:42.202472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:48.344015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-31T10:00:08.693328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:15.366907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:22.230021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:29.601181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:37.660752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:58.200752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:04.081702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:08.192440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:12.668460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:16.464237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:20.887497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:26.727783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:36.637136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:42.543099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:48.692099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:55.226726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:01.954626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:09.146256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:15.782362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:22.663846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:30.368214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:38.031929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:58.499084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:04.506946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:08.433317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:12.889881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:16.702625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:21.128331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:27.102517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:36.940021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:42.897010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:49.031958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:55.659691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:02.335730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:09.541932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:16.183931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:23.091962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:30.860518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:38.375123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:58:58.967193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:04.823180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:08.682775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:13.119643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:16.949687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:21.365468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:28.496734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:37.305168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:43.206746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:49.376919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T09:59:56.111989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:02.694508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:09.966607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:16.590850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:23.495553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-31T10:00:31.253900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-07-31T10:01:11.542174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Daily Ingested Water (L)Time of year (month)Heat Index (HI)Diastolic BPEnvironmental temperature (C)Systolic BPWeight (kg)Patient temperatureRectal temperature (deg C)Relative HumidityExposure to sunBMIHeart / Pulse rate (b/min)AgeSkin color (flushed/normal=1, pale=0.5, cyatonic=0)Strenuous exerciseTime of dayCardiovascular disease historyDehydrationSickle Cell Trait (SCT)Exertional (1) vs classic (0)SweatingNationalitySexHot/dry skinHeat stroke
Daily Ingested Water (L)1.0000.0150.053-0.023-0.0090.075-0.0660.0100.001-0.0250.0080.048-0.035-0.051-0.0360.0060.0140.0000.0000.0470.2580.0000.0000.0000.0440.091
Time of year (month)0.0151.0000.043-0.0750.056-0.0300.0110.0720.082-0.015-0.1150.0250.0170.012-0.036-0.1520.0530.0200.0570.0000.3660.0860.1790.0240.0840.404
Heat Index (HI)0.0530.0431.000-0.3070.350-0.0310.0230.3790.4000.180-0.338-0.031-0.0270.2320.111-0.3490.0250.0790.0000.0000.3150.2100.2790.0000.3010.697
Diastolic BP-0.023-0.075-0.3071.000-0.2130.1760.013-0.274-0.276-0.0560.225-0.021-0.038-0.182-0.1020.260-0.0250.0000.2350.1820.5540.2340.2970.0000.3640.746
Environmental temperature (C)-0.0090.0560.350-0.2131.000-0.0110.0670.3800.3710.141-0.375-0.0340.0700.2660.110-0.3750.0240.1120.0000.0000.3020.1920.2530.0000.2080.616
Systolic BP0.075-0.030-0.0310.176-0.0111.0000.052-0.029-0.0460.0360.082-0.048-0.080-0.0740.0580.0530.0320.0000.2380.0000.3870.2580.2880.0460.3680.745
Weight (kg)-0.0660.0110.0230.0130.0670.0521.0000.0210.0430.0510.009-0.071-0.001-0.031-0.069-0.009-0.0210.0000.0000.0000.1910.0000.0000.0000.0000.070
Patient temperature0.0100.0720.379-0.2740.380-0.0290.0211.0000.4100.136-0.3870.0240.0160.3330.103-0.436-0.0190.0520.0000.0000.3970.2690.3080.0000.2720.843
Rectal temperature (deg C)0.0010.0820.400-0.2760.371-0.0460.0430.4101.0000.184-0.428-0.0280.0060.3590.110-0.4350.0480.0000.2150.3300.5480.3100.3990.1480.3960.988
Relative Humidity-0.025-0.0150.180-0.0560.1410.0360.0510.1360.1841.000-0.166-0.0410.0100.1010.085-0.1670.0260.0000.0000.0000.2420.2230.2520.0190.2460.649
Exposure to sun0.008-0.115-0.3380.225-0.3750.0820.009-0.387-0.428-0.1661.0000.064-0.006-0.346-0.1190.4860.0110.0470.0000.0000.4560.2950.3940.0780.3020.907
BMI0.0480.025-0.031-0.021-0.034-0.048-0.0710.024-0.028-0.0410.0641.0000.063-0.026-0.1210.077-0.0880.0000.0000.0000.1880.0000.0000.0580.0000.082
Heart / Pulse rate (b/min)-0.0350.017-0.027-0.0380.070-0.080-0.0010.0160.0060.010-0.0060.0631.0000.055-0.0490.0040.0300.0000.0000.0400.0260.1120.0000.0370.1670.000
Age-0.0510.0120.232-0.1820.266-0.074-0.0310.3330.3590.101-0.346-0.0260.0551.0000.108-0.3160.0340.0700.2730.0440.1450.2340.2910.1030.2610.692
Skin color (flushed/normal=1, pale=0.5, cyatonic=0)-0.036-0.0360.111-0.1020.1100.058-0.0690.1030.1100.085-0.119-0.121-0.0490.1081.000-0.1150.0150.0000.0000.0000.0000.1750.1730.0480.0170.440
Strenuous exercise0.006-0.152-0.3490.260-0.3750.053-0.009-0.436-0.435-0.1670.4860.0770.004-0.316-0.1151.000-0.0590.1240.0000.4930.4150.2850.3590.0000.2540.803
Time of day0.0140.0530.025-0.0250.0240.032-0.021-0.0190.0480.0260.011-0.0880.0300.0340.015-0.0591.0000.0000.0340.0000.0000.0000.0000.0000.0000.025
Cardiovascular disease history0.0000.0200.0790.0000.1120.0000.0000.0520.0000.0000.0470.0000.0000.0700.0000.1240.0001.0000.0000.0000.0000.0410.0810.0000.0000.085
Dehydration0.0000.0570.0000.2350.0000.2380.0000.0000.2150.0000.0000.0000.0000.2730.0000.0000.0340.0001.0000.0000.0000.0000.0000.0000.0500.000
Sickle Cell Trait (SCT)0.0470.0000.0000.1820.0000.0000.0000.0000.3300.0000.0000.0000.0400.0440.0000.4930.0000.0000.0001.0000.0390.0000.0000.0000.0000.000
Exertional (1) vs classic (0)0.2580.3660.3150.5540.3020.3870.1910.3970.5480.2420.4560.1880.0260.1450.0000.4150.0000.0000.0000.0391.0000.0200.1810.0000.0270.459
Sweating0.0000.0860.2100.2340.1920.2580.0000.2690.3100.2230.2950.0000.1120.2340.1750.2850.0000.0410.0000.0000.0201.0000.1060.0000.1590.305
Nationality0.0000.1790.2790.2970.2530.2880.0000.3080.3990.2520.3940.0000.0000.2910.1730.3590.0000.0810.0000.0000.1810.1061.0000.0300.1310.405
Sex0.0000.0240.0000.0000.0000.0460.0000.0000.1480.0190.0780.0580.0370.1030.0480.0000.0000.0000.0000.0000.0000.0000.0301.0000.0660.000
Hot/dry skin0.0440.0840.3010.3640.2080.3680.0000.2720.3960.2460.3020.0000.1670.2610.0170.2540.0000.0000.0500.0000.0270.1590.1310.0661.0000.336
Heat stroke0.0910.4040.6970.7460.6160.7450.0700.8430.9880.6490.9070.0820.0000.6920.4400.8030.0250.0850.0000.0000.4590.3050.4050.0000.3361.000

Missing values

2023-07-31T10:00:39.087409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-31T10:00:41.289392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Daily Ingested Water (L)Time of year (month)Cardiovascular disease historyDehydrationHeat Index (HI)Diastolic BPEnvironmental temperature (C)Sickle Cell Trait (SCT)Systolic BPWeight (kg)Patient temperatureRectal temperature (deg C)Relative HumidityExposure to sunBMIExertional (1) vs classic (0)Barometric PressureHeart / Pulse rate (b/min)AgeSweatingSkin color (flushed/normal=1, pale=0.5, cyatonic=0)Strenuous exerciseNationalitySexHot/dry skinTime of dayHeat stroke
02.8215887.00000000107.29691020.00000039.00000060.00000048.36407940.80000040.8000000.4000001.024.000000129.97166.0000038.000.0210140.511010.5506281
15.0000006.84184200105.41061090.00000037.700000120.00000052.41386338.49269138.8634820.1000000.019.624603129.9768.0000050.010.0459870.010010.6136691
25.0000004.63619700101.82306550.00000037.700000100.00000046.34450339.75506138.5735970.1000000.021.124059129.9796.0000064.000.0312990.010014.4196151
310.5000002.5898280095.92538978.00000037.700000116.00000042.13247639.27724739.7344100.1000000.020.752915129.9770.0000019.000.0526540.01009.4675151
45.0000009.91438900101.67301688.00000037.700000130.00000041.85669439.43255943.6874980.1000000.022.048364129.9788.0000021.010.0760990.010012.4603151
51.50000011.95771600101.15317674.00000037.700000107.00000053.11620836.40849638.2067810.1000000.018.704689129.9788.0000052.000.0341250.010013.9746921
68.5000002.29987900122.74920576.00000037.700000116.00000045.41511842.11502437.6928370.1000000.021.546010129.9776.0000045.000.0272790.010011.9062631
75.0000009.37958900106.282326116.00000037.700000140.00000047.57299837.15631739.4305160.1000000.020.884500129.9785.0000023.010.0063420.01109.2068401
84.1424347.00000000110.00000086.05856834.260060119.726545151.95300043.00000043.0000000.1000001.022.498142129.97110.6973427.010.0535411.011010.9721761
95.0000008.61534800103.44600244.00000026.100001126.00000045.51801138.11100038.1110000.2366670.521.081179129.97168.0000012.010.0017811.011017.0000001
Daily Ingested Water (L)Time of year (month)Cardiovascular disease historyDehydrationHeat Index (HI)Diastolic BPEnvironmental temperature (C)Sickle Cell Trait (SCT)Systolic BPWeight (kg)Patient temperatureRectal temperature (deg C)Relative HumidityExposure to sunBMIExertional (1) vs classic (0)Barometric PressureHeart / Pulse rate (b/min)AgeSweatingSkin color (flushed/normal=1, pale=0.5, cyatonic=0)Strenuous exerciseNationalitySexHot/dry skinTime of dayHeat stroke
5991.82760611.9778200057.27255185.49000535.2302850114.02926449.88118337.48436536.7983450.0346600.27907421.596785029.97164.57305644.76898210.0473690.29207711014.5021930
6002.5531708.9590830082.15560384.25228823.8070670118.60678548.84915933.95788036.7716850.1959970.26095120.435131029.97112.15087625.92299600.0680480.66575911012.1824230
6013.7244492.4407910054.24710680.42156433.3696740110.37494751.37726135.13947536.2221010.2039600.28965522.958573029.97137.00867053.51441500.0521010.58862700011.3237750
6021.8606126.3061990090.13230788.51216431.8761380112.27967148.55966436.49592437.1678640.2217300.22868422.599547029.97106.67408124.15503010.0108280.60770901010.0425930
6033.58968211.1505380099.74106282.20480929.7963790119.12435342.83232136.63851936.4411020.1294410.37575719.118234029.9790.19456942.48366300.0741840.01849500016.1078950
6042.62533211.8082610069.44484382.70525528.2760560117.02521345.17384136.82585636.991501-0.1202640.26020419.510813029.97106.86816628.95256910.0605950.03882711016.9944220
6051.0693337.3699520067.27303983.82298126.0086880116.65479353.12512937.25778837.0433120.2093990.38682718.940945029.9793.71314552.73315710.0311620.65707601012.0349630
6061.0855946.9557860053.01790285.10199532.8631250112.71327247.89868736.35470836.556138-0.0653180.25926419.156116029.97143.85890221.49665100.0152980.26090100016.5458270
6075.9160552.7419060078.66563988.76594422.3536650111.96241352.48788934.33238436.2488200.2541370.32354421.062199029.97181.95281926.42068900.0504190.30555310016.7578620
6085.6546232.2871220077.64984184.97157733.0820080110.94196551.22931434.97415236.1744930.0797140.28310419.622871029.97136.74309919.02970300.0413550.80881900014.1710930